--

15(1)2025

An approach for traffic sign recognition


Author - Affiliation:
Dat Tien Nguyen - Ho Chi Minh City Open University, Ho Chi Minh City , Vietnam
Quan Minh Vu - Ho Chi Minh City Open University, Ho Chi Minh City , Vietnam
Thanh Hoang Nguyen - Ho Chi Minh City Open University, Ho Chi Minh City , Vietnam
Khai Quang Ho - Ho Chi Minh City Open University, Ho Chi Minh City , Vietnam
Phuong Quang Luu - Ho Chi Minh City Open University, Ho Chi Minh City , Vietnam
Thanh Huu Duong - Ho Chi Minh City Open University, Ho Chi Minh City , Vietnam
Corresponding author: Thanh Huu Duong - thanh.dh@ou.edu.vn
Submitted: 06-04-2024
Accepted: 10-07-2024
Published: 13-01-2025

Abstract
This article presents a model for detecting and recognizing traffic signs based on the YOLO (You Only Look Once) algorithm. Our system can detect traffic signs in real-world scenarios, including prohibitory, stop, no entry, speed limit, regulatory, and hazardous signs. However, there are still some cases where successful recognition is not achieved. Experiments were conducted on a dataset of 29,632 images, yielding % recognition accuracy of 86.8%. The system performs well in practical environments with relatively high accuracy, yet some errors persist during detection.

Keywords
FPS: Frame Per Second; mAP: mean Average Precision; NMS: Non-Maximum Suppression; object detection; YOLO: You Only Look One

Full Text:
PDF

Cite this paper as:

Nguyen, D. T., Vu, Q. M., Nguyen, T. H., Ho, K. Q., Luu, P. Q., & Duong, T. H. An approach for traffic sign recognition. Ho Chi Minh City Open University Journal of Science – Engineering and Technology, 15(1), 58-67. doi:10.46223/HCMCOUJS.tech.en.15.1.3350.2025


References

Amjoud, A. B., & Amrouch, M. (2023). Object detection using deep learning, CNNs, and vision transformers: A review. IEEE Access, 11, 35479-35516.


Flores-Calero, M., Astudillo, C. A., Guevara, D., Maza, J., Lita, B. S., Defaz, B., Ante, J. S., Zabala-Blanco, D., & Moreno, J. M. A. (2024). Traffic sign detection and recognition using YOLO object detection algorithm: A systematic review. Mathematics, 12(2), Article 297.


Liang, L., Bao, H., Pan, W., & Pan, F. (2022). Traffic sign detection via improved sparse R-CNN for autonomous vehicles. Journal of Advanced Transportation, 2022, Article 3825532.


Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 779-788).


Saxena, S., Dey, S., Shah, M., & Gupta, S. (2023). Traffic sign detection in unconstrained environment using improved YOLOv4. Expert Systems with Applications, 238, Article 121836.


Sugiharto, A., & Harjoko, A. (2016). Traffic sign detection based on HOG and PHOG using binary SVM and k-NN. In 2016 3rd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE) (pp. 1-6).


Terven, J., Córdova-Esparza, D. M., & Romero-González, J. A. (2023). A comprehensive review of YOLO architectures in computer vision: From YOLOv1 to YOLOv8 and YOLO-NAS. Machine Learning and Knowledge Extraction, 5(4), 1680-1716.


Truong, B. Q., Truong, C. H., & Truong, D. Q. (2015). Phát hiện và nhận dạng biển báo giao thông đường bộ sử dụng đặc trưng hog và mạng nơron nhân tạo [Detection and recognition of road traffic signs using hog features and artificial neural networks]. Tạp chí Khoa học Đại học Cần Thơ, 47-54.


Yu, J., Ye, X., & Tu, Q. (2022). Traffic sign detection and recognition in multi images using a fusion model with YOLO and VGG network. IEEE Transactions on Intelligent Transportation Systems, 23(9), 16632-16642.


Zhu, Z., Liang, D., Zhang, S., Huang, X., Li, B., & Hu, S. (2016). Traffic-sign detection and classification in the wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 2110-2118).



DOI: https://doi.org/10.46223/HCMCOUJS.tech.en.15.1.3350.2025
Creative Commons License
© The Author(s) 2025. This is an open access publication under CC BY NC licence.